Mini-batch stochastic gradient descent with dynamic sample sizes
Michael R. Metel · arXiv (Cornell University) · 2017
We focus on solving constrained convex optimization problems using mini-batch stochastic gradient descent. Dynamic sample size rules are presented which ensure a descent direction with high probability. Empirical results from two applications show superior convergence compared to fixed sample implementations.